RazorRecover AI — AI-Powered Payment Failure Recovery & Revenue Risk Platform
RazorRecover AI is an AI-assisted payment recovery platform for Razorpay merchants that detects failed payments, analyzes failure context with Gemini, scores recovery risk deterministically, and routes recovery recommendations through policy guardrails and human approval before controlled Razorpay Test Mode execution.
Failed payments can create revenue leakage for SaaS, subscription, D2C, and digital businesses, but blindly retrying failed transactions can be ineffective or unsafe.
The core engineering challenge was to build a recovery system that could understand why a payment failed, determine whether recovery was appropriate, recommend a suitable intervention, and still prevent an AI model from directly controlling payment actions.
RazorRecover AI was designed around five problems:
- Payment failures need contextual diagnosis rather than blind retries.
- Different failure conditions require different recovery strategies.
- AI recommendations must not directly execute money-related actions.
- High-risk or low-confidence cases need deterministic controls and human approval.
- Every recovery decision needs an auditable record.
The system therefore separates AI reasoning from execution: Gemini can recommend an action, but a deterministic policy engine decides whether that recommendation is allowed to proceed.
- Payment Event
Razorpay Test Mode / Simulator detects a failed payment and sends the payment context to the recovery system.
- Risk & Context Analysis
The system evaluates payment history, customer context, failure reason, recovery eligibility, and risk factors.
- AI Diagnosis
Google Gemini analyzes the failure context and generates a structured recovery recommendation.
- Policy Guardrails
The recommendation passes through a deterministic policy engine that checks recovery limits, risk, confidence, retry rules, cooldowns, and escalation conditions.
- Decision Layer
- Controlled Recovery
Approved cases generate a controlled Razorpay Test Mode payment/recovery action. The AI model never directly executes financial actions.
- Verification
Razorpay webhooks are verified using HMAC-SHA256 signatures and idempotency checks before the recovery state is updated.
- Audit Trail
Recovery decisions, approvals, executions, webhook events, and final outcomes are stored in the database/Firestore for traceability.
RazorRecover AI was evaluated using a reproducible synthetic benchmark containing 500 generated payment-failure cases across 12 failure scenarios.
The current repository benchmark produced:
- •Revenue at risk modeled: ₹99,33,750.02
- •Baseline simulated recovery: ₹5,45,963.27
- •RazorRecover AI simulated recovery: ₹60,99,066.70
- •Baseline recovery rate: 5.5%
- •RazorRecover AI simulated recovery rate: 61.4%
- •Unsafe decisions blocked: 41
- •Human escalations: 120
- •Decision-vs-ground-truth accuracy: 100% (500/500)
These figures are synthetic evaluation results generated by the project's reproducible evaluation engine. They are not claims of real-world recovered merchant revenue.
The system also runs Razorpay integration in Test Mode and maintains a clear separation between synthetic evaluation data, demo/seed data, and Razorpay Test Mode transactions.